Overview

Dataset statistics

Number of variables54
Number of observations5527754
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 GiB
Average record size in memory548.3 B

Variable types

Numeric24
Categorical30

Alerts

BoolBridle has constant value "0"Constant
Town has a high cardinality: 1884 distinct valuesHigh cardinality
PipeId is highly overall correlated with Autonomía_CataluñaHigh correlation
No_Inspections is highly overall correlated with gas_naturalHigh correlation
Probability_rate is highly overall correlated with preventive_maintenance_rate and 5 other fieldsHigh correlation
preventive_maintenance_rate is highly overall correlated with Probability_rate and 5 other fieldsHigh correlation
Age_pipe_at_inspection is highly overall correlated with YearBuiltHigh correlation
Average_MonthsLastRev is highly overall correlated with MonthsLastRevHigh correlation
MonthsLastRev is highly overall correlated with Average_MonthsLastRevHigh correlation
relative_risk is highly overall correlated with Probability_rate and 4 other fieldsHigh correlation
average_severity_pipe is highly overall correlated with Probability_rate and 2 other fieldsHigh correlation
YearBuilt is highly overall correlated with Age_pipe_at_inspectionHigh correlation
Diameter is highly overall correlated with Relative_Thickness and 1 other fieldsHigh correlation
Length is highly overall correlated with NumConnections and 3 other fieldsHigh correlation
Pressure is highly overall correlated with aspect and 4 other fieldsHigh correlation
NumConnections is highly overall correlated with Length and 3 other fieldsHigh correlation
aspect is highly overall correlated with Pressure and 3 other fieldsHigh correlation
Relative_Thickness is highly overall correlated with Diameter and 4 other fieldsHigh correlation
pipe_area is highly overall correlated with Length and 2 other fieldsHigh correlation
area_connection is highly overall correlated with NumConnectionsHigh correlation
Diameter2 is highly overall correlated with Diameter and 1 other fieldsHigh correlation
Length2 is highly overall correlated with Length and 2 other fieldsHigh correlation
Pressure2 is highly overall correlated with Pressure and 2 other fieldsHigh correlation
TownCount is highly overall correlated with Province and 2 other fieldsHigh correlation
No_Incidents is highly overall correlated with Probability_rate and 3 other fieldsHigh correlation
Incidence is highly overall correlated with Probability_rate and 4 other fieldsHigh correlation
connection_bool is highly overall correlated with Length and 1 other fieldsHigh correlation
Severity_low is highly overall correlated with Probability_rate and 2 other fieldsHigh correlation
gas_natural is highly overall correlated with No_InspectionsHigh correlation
Material_Acrylonitrile-Butadiene-Styrene is highly overall correlated with Pressure and 1 other fieldsHigh correlation
Material_Polyethylene is highly overall correlated with Pressure and 1 other fieldsHigh correlation
Province is highly overall correlated with TownCount and 10 other fieldsHigh correlation
Autonomía_Andalucía is highly overall correlated with ProvinceHigh correlation
Autonomía_Aragón is highly overall correlated with ProvinceHigh correlation
Autonomía_Castilla y León is highly overall correlated with ProvinceHigh correlation
Autonomía_Castilla-La Mancha is highly overall correlated with ProvinceHigh correlation
Autonomía_Cataluña is highly overall correlated with PipeId and 1 other fieldsHigh correlation
Autonomía_Comunidad Valenciana is highly overall correlated with ProvinceHigh correlation
Autonomía_Galicia is highly overall correlated with ProvinceHigh correlation
Autonomía_Madrid (Comunidad de) is highly overall correlated with TownCount and 2 other fieldsHigh correlation
Autonomía_Rioja (La) is highly overall correlated with ProvinceHigh correlation
Madrid is highly overall correlated with TownCount and 2 other fieldsHigh correlation
No_Incidents is highly imbalanced (97.7%)Imbalance
Incidence is highly imbalanced (98.5%)Imbalance
NumConnectionsUnder is highly imbalanced (99.9%)Imbalance
Severity_high is highly imbalanced (99.8%)Imbalance
Severity_medium is highly imbalanced (99.7%)Imbalance
Severity_low is highly imbalanced (98.8%)Imbalance
gas_natural is highly imbalanced (87.1%)Imbalance
Material_Acrylonitrile-Butadiene-Styrene is highly imbalanced (64.1%)Imbalance
Material_Copper is highly imbalanced (97.6%)Imbalance
Material_Fiberglass-Reinforced Plastic is highly imbalanced (86.0%)Imbalance
Material_Polyethylene is highly imbalanced (54.9%)Imbalance
Material_Polypropylene is highly imbalanced (96.7%)Imbalance
Autonomía_Andalucía is highly imbalanced (56.7%)Imbalance
Autonomía_Aragón is highly imbalanced (96.0%)Imbalance
Autonomía_Balears (Illes) is highly imbalanced (98.7%)Imbalance
Autonomía_Castilla y León is highly imbalanced (58.2%)Imbalance
Autonomía_Castilla-La Mancha is highly imbalanced (64.8%)Imbalance
Autonomía_Extremadura is highly imbalanced (98.9%)Imbalance
Autonomía_Galicia is highly imbalanced (64.4%)Imbalance
Autonomía_Madrid (Comunidad de) is highly imbalanced (55.3%)Imbalance
Autonomía_Navarra (Comunidad Foral de) is highly imbalanced (99.8%)Imbalance
Autonomía_Rioja (La) is highly imbalanced (89.4%)Imbalance
Madrid is highly imbalanced (65.8%)Imbalance
Probability_rate is highly skewed (γ1 = 22.60196822)Skewed
average_severity_pipe is highly skewed (γ1 = -27.16596007)Skewed
aspect is highly skewed (γ1 = 1145.0382)Skewed
area_connection is highly skewed (γ1 = 465.9658633)Skewed
incidence_area is highly skewed (γ1 = 451.9992111)Skewed
Probability_rate has 5495997 (99.4%) zerosZeros
preventive_maintenance_rate has 5495997 (99.4%) zerosZeros
relative_risk has 5495997 (99.4%) zerosZeros
NumConnections has 3452531 (62.5%) zerosZeros
area_connection has 3452531 (62.5%) zerosZeros
incidence_area has 5519945 (99.9%) zerosZeros

Reproduction

Analysis started2023-02-17 17:51:24.903642
Analysis finished2023-02-17 18:21:08.942137
Duration29 minutes and 44.04 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PipeId
Real number (ℝ)

Distinct1239294
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0363602 × 108
Minimum489616
Maximum4.5199531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:09.048859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum489616
5-th percentile12108374
Q11.335693 × 108
median1.9030265 × 108
Q32.9861368 × 108
95-th percentile3.9845461 × 108
Maximum4.5199531 × 108
Range4.5150569 × 108
Interquartile range (IQR)1.6504437 × 108

Descriptive statistics

Standard deviation1.1442596 × 108
Coefficient of variation (CV)0.56191416
Kurtosis-0.74810132
Mean2.0363602 × 108
Median Absolute Deviation (MAD)94305870
Skewness-0.084886108
Sum1.1256498 × 1015
Variance1.3093301 × 1016
MonotonicityNot monotonic
2023-02-17T19:21:09.169451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388050077 14
 
< 0.1%
387643872 14
 
< 0.1%
387044586 14
 
< 0.1%
384036391 14
 
< 0.1%
258601675 14
 
< 0.1%
294881473 14
 
< 0.1%
258601692 14
 
< 0.1%
384036104 14
 
< 0.1%
307722970 14
 
< 0.1%
388235914 14
 
< 0.1%
Other values (1239284) 5527614
> 99.9%
ValueCountFrequency (%)
489616 5
< 0.1%
489645 5
< 0.1%
489780 5
< 0.1%
489790 5
< 0.1%
489792 5
< 0.1%
489793 5
< 0.1%
489981 5
< 0.1%
489982 5
< 0.1%
489996 5
< 0.1%
490308 4
< 0.1%
ValueCountFrequency (%)
451995309 4
< 0.1%
451995260 4
< 0.1%
451995254 2
< 0.1%
451195406 3
< 0.1%
451195391 4
< 0.1%
451195364 3
< 0.1%
451195284 4
< 0.1%
451194879 4
< 0.1%
451194601 4
< 0.1%
451194531 4
< 0.1%

No_Inspections
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8998146
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:09.270210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median5
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0456331
Coefficient of variation (CV)0.21340259
Kurtosis4.9956389
Mean4.8998146
Median Absolute Deviation (MAD)0
Skewness-0.77505844
Sum27084970
Variance1.0933487
MonotonicityNot monotonic
2023-02-17T19:21:09.353958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 3460902
62.6%
6 1044568
 
18.9%
4 432360
 
7.8%
3 272844
 
4.9%
2 201846
 
3.7%
1 55048
 
1.0%
7 27790
 
0.5%
10 15900
 
0.3%
8 5834
 
0.1%
9 5572
 
0.1%
ValueCountFrequency (%)
1 55048
 
1.0%
2 201846
 
3.7%
3 272844
 
4.9%
4 432360
 
7.8%
5 3460902
62.6%
6 1044568
 
18.9%
7 27790
 
0.5%
8 5834
 
0.1%
9 5572
 
0.1%
10 15900
 
0.3%
ValueCountFrequency (%)
11 5090
 
0.1%
10 15900
 
0.3%
9 5572
 
0.1%
8 5834
 
0.1%
7 27790
 
0.5%
6 1044568
 
18.9%
5 3460902
62.6%
4 432360
 
7.8%
3 272844
 
4.9%
2 201846
 
3.7%

No_Incidents
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.3 MiB
0.0
5495997 
1.0
 
29763
2.0
 
1888
3.0
 
94
4.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16583262
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5495997
99.4%
1.0 29763
 
0.5%
2.0 1888
 
< 0.1%
3.0 94
 
< 0.1%
4.0 12
 
< 0.1%

Length

2023-02-17T19:21:09.445211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:09.556742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5495997
99.4%
1.0 29763
 
0.5%
2.0 1888
 
< 0.1%
3.0 94
 
< 0.1%
4.0 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11023751
66.5%
. 5527754
33.3%
1 29763
 
0.2%
2 1888
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11055508
66.7%
Other Punctuation 5527754
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11023751
99.7%
1 29763
 
0.3%
2 1888
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5527754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16583262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11023751
66.5%
. 5527754
33.3%
1 29763
 
0.2%
2 1888
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16583262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11023751
66.5%
. 5527754
33.3%
1 29763
 
0.2%
2 1888
 
< 0.1%
3 94
 
< 0.1%
4 12
 
< 0.1%

InspectionYear
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5875
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:09.640800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2016
Q32018
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9040477
Coefficient of variation (CV)0.0014407947
Kurtosis-1.178031
Mean2015.5875
Median Absolute Deviation (MAD)2
Skewness-0.094635294
Sum1.1141672 × 1010
Variance8.4334929
MonotonicityNot monotonic
2023-02-17T19:21:09.726435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2019 599879
10.9%
2017 599289
10.8%
2018 565109
10.2%
2020 561162
10.2%
2016 547594
9.9%
2015 543647
9.8%
2012 534717
9.7%
2014 519545
9.4%
2013 511353
9.3%
2011 461998
8.4%
Other values (2) 83461
 
1.5%
ValueCountFrequency (%)
2010 82345
 
1.5%
2011 461998
8.4%
2012 534717
9.7%
2013 511353
9.3%
2014 519545
9.4%
2015 543647
9.8%
2016 547594
9.9%
2017 599289
10.8%
2018 565109
10.2%
2019 599879
10.9%
ValueCountFrequency (%)
2021 1116
 
< 0.1%
2020 561162
10.2%
2019 599879
10.9%
2018 565109
10.2%
2017 599289
10.8%
2016 547594
9.9%
2015 543647
9.8%
2014 519545
9.4%
2013 511353
9.3%
2012 534717
9.7%

Probability_rate
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0014160483
Minimum0
Maximum1
Zeros5495997
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:09.821075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.021571967
Coefficient of variation (CV)15.23392
Kurtosis715.98345
Mean0.0014160483
Median Absolute Deviation (MAD)0
Skewness22.601968
Sum7827.5667
Variance0.00046534977
MonotonicityNot monotonic
2023-02-17T19:21:09.912217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 5495997
99.4%
0.2 14326
 
0.3%
0.1666666667 8905
 
0.2%
0.3333333333 2631
 
< 0.1%
0.5 2342
 
< 0.1%
0.25 1392
 
< 0.1%
0.4 623
 
< 0.1%
1 604
 
< 0.1%
0.1428571429 560
 
< 0.1%
0.6666666667 135
 
< 0.1%
Other values (6) 239
 
< 0.1%
ValueCountFrequency (%)
0 5495997
99.4%
0.1 40
 
< 0.1%
0.1111111111 27
 
< 0.1%
0.125 72
 
< 0.1%
0.1428571429 560
 
< 0.1%
0.1666666667 8905
 
0.2%
0.2 14326
 
0.3%
0.25 1392
 
< 0.1%
0.2857142857 56
 
< 0.1%
0.3333333333 2631
 
< 0.1%
ValueCountFrequency (%)
1 604
 
< 0.1%
0.75 4
 
< 0.1%
0.6666666667 135
 
< 0.1%
0.6 40
 
< 0.1%
0.5 2342
 
< 0.1%
0.4 623
 
< 0.1%
0.3333333333 2631
 
< 0.1%
0.2857142857 56
 
< 0.1%
0.25 1392
 
< 0.1%
0.2 14326
0.3%

preventive_maintenance_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0050816641
Minimum0
Maximum3
Zeros5495997
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:10.019805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.074160878
Coefficient of variation (CV)14.593817
Kurtosis480.23403
Mean0.0050816641
Median Absolute Deviation (MAD)0
Skewness19.184705
Sum28090.189
Variance0.0054998358
MonotonicityNot monotonic
2023-02-17T19:21:10.130886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5495997
99.4%
0.76 10024
 
0.2%
0.6388888889 6955
 
0.1%
0.72 2965
 
0.1%
1.222222222 2150
 
< 0.1%
1.75 2100
 
< 0.1%
0.68 1337
 
< 0.1%
0.9375 1082
 
< 0.1%
0.6111111111 998
 
< 0.1%
0.5833333333 952
 
< 0.1%
Other values (34) 3194
 
0.1%
ValueCountFrequency (%)
0 5495997
99.4%
0.38 10
 
< 0.1%
0.39 30
 
< 0.1%
0.4197530864 9
 
< 0.1%
0.4320987654 18
 
< 0.1%
0.46875 16
 
< 0.1%
0.484375 56
 
< 0.1%
0.5102040816 49
 
< 0.1%
0.5306122449 84
 
< 0.1%
0.5510204082 427
 
< 0.1%
ValueCountFrequency (%)
3 503
 
< 0.1%
2.666666667 2
 
< 0.1%
2.5 8
 
< 0.1%
2.4375 4
 
< 0.1%
2.222222222 116
 
< 0.1%
2.04 30
 
< 0.1%
2 73
 
< 0.1%
1.92 10
 
< 0.1%
1.777777778 6
 
< 0.1%
1.75 2100
< 0.1%

Age_pipe_at_inspection
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.934826
Minimum0
Maximum34
Zeros47920
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:10.248175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median12
Q318
95-th percentile26
Maximum34
Range34
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3678502
Coefficient of variation (CV)0.56961342
Kurtosis-0.3881397
Mean12.934826
Median Absolute Deviation (MAD)5
Skewness0.42706491
Sum71500534
Variance54.285216
MonotonicityNot monotonic
2023-02-17T19:21:10.353807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
12 294120
 
5.3%
11 291642
 
5.3%
13 285283
 
5.2%
10 284764
 
5.2%
14 268982
 
4.9%
9 268032
 
4.8%
15 259605
 
4.7%
8 257124
 
4.7%
16 240189
 
4.3%
7 229287
 
4.1%
Other values (25) 2848726
51.5%
ValueCountFrequency (%)
0 47920
 
0.9%
1 147548
2.7%
2 171216
3.1%
3 189886
3.4%
4 213709
3.9%
5 200408
3.6%
6 225015
4.1%
7 229287
4.1%
8 257124
4.7%
9 268032
4.8%
ValueCountFrequency (%)
34 10238
 
0.2%
33 14486
 
0.3%
32 23193
 
0.4%
31 28606
 
0.5%
30 37864
0.7%
29 42874
0.8%
28 55527
1.0%
27 63556
1.1%
26 76875
1.4%
25 84102
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
1
4764484 
0
763270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Length

2023-02-17T19:21:10.459801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:10.552128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Most occurring characters

ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4764484
86.2%
0 763270
 
13.8%

Average_MonthsLastRev
Real number (ℝ)

Distinct523
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.117873
Minimum0
Maximum77.5
Zeros652
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:10.648890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q122.166667
median23.75
Q324
95-th percentile24.333333
Maximum77.5
Range77.5
Interquartile range (IQR)1.8333333

Descriptive statistics

Standard deviation2.3128741
Coefficient of variation (CV)0.10004701
Kurtosis23.285797
Mean23.117873
Median Absolute Deviation (MAD)0.45
Skewness0.41638573
Sum1.2778991 × 108
Variance5.3493865
MonotonicityNot monotonic
2023-02-17T19:21:10.774297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1790833
32.4%
24.2 406656
 
7.4%
23.6 305635
 
5.5%
22 257153
 
4.7%
23.8 189153
 
3.4%
23.4 160775
 
2.9%
23 155075
 
2.8%
21 110285
 
2.0%
23.2 109843
 
2.0%
22.2 102428
 
1.9%
Other values (513) 1939918
35.1%
ValueCountFrequency (%)
0 652
< 0.1%
1 81
 
< 0.1%
2 117
 
< 0.1%
3 246
 
< 0.1%
4 288
< 0.1%
5 358
< 0.1%
6 393
< 0.1%
6.5 16
 
< 0.1%
7 324
< 0.1%
7.5 28
 
< 0.1%
ValueCountFrequency (%)
77.5 1
 
< 0.1%
76 1
 
< 0.1%
75 1
 
< 0.1%
70 2
 
< 0.1%
69 1
 
< 0.1%
68.5 2
 
< 0.1%
67.5 3
 
< 0.1%
67 4
 
< 0.1%
66.5 5
 
< 0.1%
66 49
< 0.1%

MonthsLastRev
Real number (ℝ)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.984021
Minimum0
Maximum39
Zeros1019
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:10.905243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q123
median24
Q324
95-th percentile25
Maximum39
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0803019
Coefficient of variation (CV)0.13401928
Kurtosis14.248788
Mean22.984021
Median Absolute Deviation (MAD)0
Skewness-2.958186
Sum1.2705001 × 108
Variance9.4882598
MonotonicityNot monotonic
2023-02-17T19:21:11.020833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
24 2989018
54.1%
23 779716
 
14.1%
22 481052
 
8.7%
25 458181
 
8.3%
21 249683
 
4.5%
20 79949
 
1.4%
26 45737
 
0.8%
18 42390
 
0.8%
19 42130
 
0.8%
17 35422
 
0.6%
Other values (30) 324476
 
5.9%
ValueCountFrequency (%)
0 1019
 
< 0.1%
1 1692
 
< 0.1%
2 2465
 
< 0.1%
3 4475
 
0.1%
4 4877
 
0.1%
5 8163
0.1%
6 10052
0.2%
7 9855
0.2%
8 10010
0.2%
9 17002
0.3%
ValueCountFrequency (%)
39 1201
 
< 0.1%
38 446
 
< 0.1%
37 1015
 
< 0.1%
36 5747
0.1%
35 2768
 
0.1%
34 3685
 
0.1%
33 3650
 
0.1%
32 8010
0.1%
31 4375
 
0.1%
30 12449
0.2%

relative_risk
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022475708
Minimum0
Maximum13.333333
Zeros5495997
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:11.145142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13.333333
Range13.333333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30339287
Coefficient of variation (CV)13.498701
Kurtosis239.36284
Mean0.022475708
Median Absolute Deviation (MAD)0
Skewness14.600915
Sum124240.18
Variance0.092047232
MonotonicityNot monotonic
2023-02-17T19:21:11.267692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5495997
99.4%
3.8 10034
 
0.2%
3.833333333 6955
 
0.1%
3.5 3085
 
0.1%
3.6 2955
 
0.1%
3.666666667 2508
 
< 0.1%
3.4 1337
 
< 0.1%
3.75 1098
 
< 0.1%
3 667
 
< 0.1%
7.333333333 640
 
< 0.1%
Other values (34) 2478
 
< 0.1%
ValueCountFrequency (%)
0 5495997
99.4%
1 30
 
< 0.1%
2 56
 
< 0.1%
2.5 58
 
< 0.1%
3 667
 
< 0.1%
3.25 125
 
< 0.1%
3.333333333 183
 
< 0.1%
3.4 1337
 
< 0.1%
3.5 3085
 
0.1%
3.571428571 49
 
< 0.1%
ValueCountFrequency (%)
13.33333333 6
 
< 0.1%
12 6
 
< 0.1%
10.5 42
 
< 0.1%
10.2 30
 
< 0.1%
9.75 4
 
< 0.1%
9.6 10
 
< 0.1%
9 6
 
< 0.1%
8 2
 
< 0.1%
7.428571429 35
 
< 0.1%
7.333333333 640
< 0.1%

average_severity_pipe
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9981682
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:11.378159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.029360141
Coefficient of variation (CV)0.0073433982
Kurtosis1294.6955
Mean3.9981682
Median Absolute Deviation (MAD)0
Skewness-27.16596
Sum22100890
Variance0.0008620179
MonotonicityNot monotonic
2023-02-17T19:21:11.474065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4 5495997
99.4%
3.8 10034
 
0.2%
3.833333333 6955
 
0.1%
3.6 3376
 
0.1%
3.5 3350
 
0.1%
3.666666667 3148
 
0.1%
3.4 1440
 
< 0.1%
3.75 1098
 
< 0.1%
3 820
 
< 0.1%
3.857142857 427
 
< 0.1%
Other values (16) 1109
 
< 0.1%
ValueCountFrequency (%)
1 30
 
< 0.1%
1.5 1
 
< 0.1%
2 60
 
< 0.1%
2.5 66
 
< 0.1%
2.666666667 8
 
< 0.1%
2.8 15
 
< 0.1%
3 820
< 0.1%
3.166666667 12
 
< 0.1%
3.2 105
 
< 0.1%
3.25 158
 
< 0.1%
ValueCountFrequency (%)
4 5495997
99.4%
3.9 30
 
< 0.1%
3.888888889 18
 
< 0.1%
3.875 56
 
< 0.1%
3.857142857 427
 
< 0.1%
3.833333333 6955
 
0.1%
3.8 10034
 
0.2%
3.777777778 9
 
< 0.1%
3.75 1098
 
< 0.1%
3.714285714 119
 
< 0.1%

Incidence
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size316.3 MiB
0.0
5519945 
1.0
 
7809

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16583262
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5519945
99.9%
1.0 7809
 
0.1%

Length

2023-02-17T19:21:11.572166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:11.664435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5519945
99.9%
1.0 7809
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11047699
66.6%
. 5527754
33.3%
1 7809
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11055508
66.7%
Other Punctuation 5527754
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11047699
99.9%
1 7809
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 5527754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16583262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11047699
66.6%
. 5527754
33.3%
1 7809
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16583262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11047699
66.6%
. 5527754
33.3%
1 7809
 
< 0.1%

YearBuilt
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.6526
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:11.752769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1989
Q11998
median2003
Q32008
95-th percentile2014
Maximum2020
Range37
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.200412
Coefficient of variation (CV)0.0035954373
Kurtosis-0.29095334
Mean2002.6526
Median Absolute Deviation (MAD)5
Skewness-0.28213615
Sum1.1070171 × 1010
Variance51.845933
MonotonicityNot monotonic
2023-02-17T19:21:11.865915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2002 377039
 
6.8%
2004 333779
 
6.0%
2005 301508
 
5.5%
2003 294446
 
5.3%
2001 290790
 
5.3%
2006 284835
 
5.2%
2008 284419
 
5.1%
2007 264959
 
4.8%
1999 260904
 
4.7%
2000 250976
 
4.5%
Other values (28) 2584099
46.7%
ValueCountFrequency (%)
1983 13021
 
0.2%
1984 16160
 
0.3%
1985 23709
 
0.4%
1986 37662
 
0.7%
1987 56511
1.0%
1988 70978
1.3%
1989 73552
1.3%
1990 73740
1.3%
1991 76985
1.4%
1992 104464
1.9%
ValueCountFrequency (%)
2020 1244
 
< 0.1%
2019 5909
 
0.1%
2018 15495
 
0.3%
2017 31795
 
0.6%
2016 103469
1.9%
2015 113161
2.0%
2014 97402
1.8%
2013 107930
2.0%
2012 143821
2.6%
2011 149157
2.7%

Diameter
Real number (ℝ)

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11711464
Minimum0.01
Maximum0.6096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:11.997136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.063
Q10.09
median0.11
Q30.16
95-th percentile0.2
Maximum0.6096
Range0.5996
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.054578394
Coefficient of variation (CV)0.46602535
Kurtosis3.5586866
Mean0.11711464
Median Absolute Deviation (MAD)0.047
Skewness1.397711
Sum647380.95
Variance0.0029788011
MonotonicityNot monotonic
2023-02-17T19:21:12.119226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 1486633
26.9%
0.063 1048452
19.0%
0.09 1015013
18.4%
0.16 754563
13.7%
0.2 519279
 
9.4%
0.04 106468
 
1.9%
0.1524 88428
 
1.6%
0.1016 72764
 
1.3%
0.25 67799
 
1.2%
0.2032 63532
 
1.1%
Other values (51) 304823
 
5.5%
ValueCountFrequency (%)
0.01 158
 
< 0.1%
0.011 60
 
< 0.1%
0.012 1334
 
< 0.1%
0.0127 11
 
< 0.1%
0.013 105
 
< 0.1%
0.014 43
 
< 0.1%
0.015 4246
0.1%
0.016 1070
 
< 0.1%
0.018 6
 
< 0.1%
0.019 3325
0.1%
ValueCountFrequency (%)
0.6096 67
 
< 0.1%
0.5588 46
 
< 0.1%
0.508 2404
 
< 0.1%
0.5 102
 
< 0.1%
0.4572 1853
 
< 0.1%
0.4064 7656
0.1%
0.4 561
 
< 0.1%
0.3556 474
 
< 0.1%
0.355 108
 
< 0.1%
0.35 372
 
< 0.1%

Length
Real number (ℝ)

Distinct124031
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.996803
Minimum0.005
Maximum105.748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:12.248102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile1.002
Q13.679
median11.918
Q335.228
95-th percentile79.588
Maximum105.748
Range105.743
Interquartile range (IQR)31.549

Descriptive statistics

Standard deviation25.338633
Coefficient of variation (CV)1.1018329
Kurtosis0.9205847
Mean22.996803
Median Absolute Deviation (MAD)10.018
Skewness1.3389394
Sum1.2712067 × 108
Variance642.04632
MonotonicityNot monotonic
2023-02-17T19:21:12.367781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 26510
 
0.5%
1 25323
 
0.5%
0.5 18375
 
0.3%
1.5 18041
 
0.3%
1.002 16884
 
0.3%
1.003 10153
 
0.2%
1.001 9546
 
0.2%
3 8694
 
0.2%
2.004 8567
 
0.2%
1.2 8287
 
0.1%
Other values (124021) 5377374
97.3%
ValueCountFrequency (%)
0.005 14
 
< 0.1%
0.006 19
 
< 0.1%
0.007 14
 
< 0.1%
0.008 20
 
< 0.1%
0.009 19
 
< 0.1%
0.01 61
< 0.1%
0.011 25
< 0.1%
0.012 12
 
< 0.1%
0.013 31
< 0.1%
0.014 8
 
< 0.1%
ValueCountFrequency (%)
105.748 8
 
< 0.1%
105.746 10
< 0.1%
105.745 20
< 0.1%
105.743 6
 
< 0.1%
105.742 18
< 0.1%
105.741 5
 
< 0.1%
105.739 11
< 0.1%
105.738 5
 
< 0.1%
105.737 5
 
< 0.1%
105.736 13
< 0.1%

Pressure
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6359134
Minimum0.025
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:12.476214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.025
Q10.1
median0.15
Q34
95-th percentile16
Maximum80
Range79.975
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation5.9673826
Coefficient of variation (CV)2.2638766
Kurtosis53.463477
Mean2.6359134
Median Absolute Deviation (MAD)0.125
Skewness6.2536418
Sum14570681
Variance35.609656
MonotonicityNot monotonic
2023-02-17T19:21:12.568923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 1605762
29.0%
0.15 1559337
28.2%
0.025 1038331
18.8%
0.1 562184
 
10.2%
16 251041
 
4.5%
5 159352
 
2.9%
0.4 143799
 
2.6%
1.7 112953
 
2.0%
49.5 29224
 
0.5%
0.05 19994
 
0.4%
Other values (10) 45777
 
0.8%
ValueCountFrequency (%)
0.025 1038331
18.8%
0.05 19994
 
0.4%
0.1 562184
 
10.2%
0.15 1559337
28.2%
0.4 143799
 
2.6%
1.7 112953
 
2.0%
2 11816
 
0.2%
4 1605762
29.0%
5 159352
 
2.9%
10 6189
 
0.1%
ValueCountFrequency (%)
80 2391
 
< 0.1%
72 4851
 
0.1%
59.5 2784
 
0.1%
49.5 29224
 
0.5%
45 2085
 
< 0.1%
40 667
 
< 0.1%
36 9169
 
0.2%
25 677
 
< 0.1%
16 251041
4.5%
12 5148
 
0.1%

NumConnections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77901115
Minimum0
Maximum34
Zeros3452531
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:12.681854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5070289
Coefficient of variation (CV)1.9345409
Kurtosis22.453819
Mean0.77901115
Median Absolute Deviation (MAD)0
Skewness3.7073377
Sum4306182
Variance2.2711362
MonotonicityNot monotonic
2023-02-17T19:21:12.787233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3452531
62.5%
1 1130476
 
20.5%
2 449447
 
8.1%
3 205495
 
3.7%
4 114210
 
2.1%
5 64739
 
1.2%
6 40059
 
0.7%
7 23399
 
0.4%
8 15997
 
0.3%
9 10360
 
0.2%
Other values (24) 21041
 
0.4%
ValueCountFrequency (%)
0 3452531
62.5%
1 1130476
 
20.5%
2 449447
 
8.1%
3 205495
 
3.7%
4 114210
 
2.1%
5 64739
 
1.2%
6 40059
 
0.7%
7 23399
 
0.4%
8 15997
 
0.3%
9 10360
 
0.2%
ValueCountFrequency (%)
34 5
 
< 0.1%
33 10
 
< 0.1%
32 2
 
< 0.1%
30 10
 
< 0.1%
29 17
 
< 0.1%
28 38
< 0.1%
27 19
< 0.1%
26 29
< 0.1%
25 17
 
< 0.1%
24 44
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5526923 
1
 
811
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Length

2023-02-17T19:21:12.895005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:12.990882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5526923
> 99.9%
1 811
 
< 0.1%
2 20
 
< 0.1%

BoolBridle
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5527754 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5527754
100.0%

Length

2023-02-17T19:21:13.072328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:13.161228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5527754
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5527754
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5527754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5527754
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5527754
100.0%

aspect
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct630733
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6074411
Minimum0.00060597981
Maximum177165.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:13.253200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00060597981
5-th percentile0.0053777319
Q10.040479275
median0.37411533
Q32.8811573
95-th percentile31.682666
Maximum177165.35
Range177165.35
Interquartile range (IQR)2.840678

Descriptive statistics

Standard deviation121.10349
Coefficient of variation (CV)15.919084
Kurtosis1658321.7
Mean7.6074411
Median Absolute Deviation (MAD)0.36447697
Skewness1145.0382
Sum42052063
Variance14666.056
MonotonicityNot monotonic
2023-02-17T19:21:13.371735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9090909091 4616
 
0.1%
63.36533283 3981
 
0.1%
0.6818181818 3682
 
0.1%
1.363636364 3048
 
0.1%
31.74603175 2905
 
0.1%
63.49206349 2774
 
0.1%
22.22222222 2699
 
< 0.1%
1.875 2599
 
< 0.1%
0.625 2217
 
< 0.1%
0.46875 2122
 
< 0.1%
Other values (630723) 5497111
99.4%
ValueCountFrequency (%)
0.0006059798088 5
< 0.1%
0.0006389939679 5
< 0.1%
0.0006502790519 10
< 0.1%
0.0006605229228 6
< 0.1%
0.0006862023913 5
< 0.1%
0.0006912722728 5
< 0.1%
0.0007513142364 6
< 0.1%
0.0007527322676 6
< 0.1%
0.0007535827964 5
< 0.1%
0.0007541843278 6
< 0.1%
ValueCountFrequency (%)
177165.3543 2
 
< 0.1%
25000 1
 
< 0.1%
24242.42424 1
 
< 0.1%
20997.37533 1
 
< 0.1%
14173.22835 1
 
< 0.1%
13227.51323 4
< 0.1%
13123.35958 2
 
< 0.1%
12698.4127 7
< 0.1%
12500 2
 
< 0.1%
10582.01058 4
< 0.1%

Relative_Thickness
Real number (ℝ)

Distinct302
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5430606
Minimum0.00035277778
Maximum24.384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:13.499968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.00035277778
5-th percentile0.01
Q10.0225
median0.6
Q31.1
95-th percentile8
Maximum24.384
Range24.383647
Interquartile range (IQR)1.0775

Descriptive statistics

Standard deviation2.5296167
Coefficient of variation (CV)1.6393502
Kurtosis3.8151648
Mean1.5430606
Median Absolute Deviation (MAD)0.5775
Skewness2.0836666
Sum8529659.5
Variance6.3989606
MonotonicityNot monotonic
2023-02-17T19:21:13.622942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7333333333 640450
 
11.6%
0.01575 601313
 
10.9%
0.0225 482236
 
8.7%
4.4 340519
 
6.2%
6.4 305983
 
5.5%
0.6 285971
 
5.2%
1.066666667 250456
 
4.5%
0.0275 227225
 
4.1%
8 201685
 
3.6%
0.42 196435
 
3.6%
Other values (292) 1995481
36.1%
ValueCountFrequency (%)
0.0003527777778 36
 
< 0.1%
0.0005291666667 35
 
< 0.1%
0.0005644444444 1
 
< 0.1%
0.000635 6
 
< 0.1%
0.0007055555556 108
< 0.1%
0.0007696969697 18
 
< 0.1%
0.0008537815126 171
< 0.1%
0.0008819444444 5
 
< 0.1%
0.0009525 100
 
< 0.1%
0.001026262626 256
< 0.1%
ValueCountFrequency (%)
24.384 26
 
< 0.1%
20.32 32
 
< 0.1%
20 102
 
< 0.1%
18.288 8
 
< 0.1%
16.256 490
 
< 0.1%
16 540
 
< 0.1%
14.224 10
 
< 0.1%
14.2 11
 
< 0.1%
14 372
 
< 0.1%
12.6 30008
0.5%

pipe_area
Real number (ℝ)

Distinct408200
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.552833
Minimum0.000502656
Maximum189.60639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:13.747162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.000502656
5-th percentile0.28817268
Q11.1873269
median3.9659558
Q311.743489
95-th percentile31.243589
Maximum189.60639
Range189.60589
Interquartile range (IQR)10.556162

Descriptive statistics

Standard deviation11.260104
Coefficient of variation (CV)1.316535
Kurtosis9.4298947
Mean8.552833
Median Absolute Deviation (MAD)3.3571138
Skewness2.5234955
Sum47277957
Variance126.78995
MonotonicityNot monotonic
2023-02-17T19:21:13.866203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.691152 6984
 
0.1%
0.345576 6337
 
0.1%
0.518364 6286
 
0.1%
0.1983166416 5889
 
0.1%
0.251328 5559
 
0.1%
0.1979208 5404
 
0.1%
0.565488 5310
 
0.1%
0.3958416 4974
 
0.1%
0.282744 4575
 
0.1%
1.005312 4239
 
0.1%
Other values (408190) 5472197
99.0%
ValueCountFrequency (%)
0.000502656 1
 
< 0.1%
0.000518364 1
 
< 0.1%
0.0006031872 5
< 0.1%
0.0007979664 2
 
< 0.1%
0.000989604 7
< 0.1%
0.001005312 2
 
< 0.1%
0.0011875248 8
< 0.1%
0.0012534984 1
 
< 0.1%
0.00141372 2
 
< 0.1%
0.00143633952 1
 
< 0.1%
ValueCountFrequency (%)
189.6063922 5
< 0.1%
167.5825196 1
 
< 0.1%
166.409509 4
< 0.1%
166.3345001 6
< 0.1%
166.3265205 5
< 0.1%
164.9856177 6
< 0.1%
164.1991421 4
< 0.1%
163.4761845 5
< 0.1%
162.9718697 4
< 0.1%
155.8460298 5
< 0.1%

area_connection
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct294433
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19393303
Minimum0
Maximum1989.4321
Zeros3452531
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:13.996710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.13199728
95-th percentile0.6884723
Maximum1989.4321
Range1989.4321
Interquartile range (IQR)0.13199728

Descriptive statistics

Standard deviation2.0638916
Coefficient of variation (CV)10.64229
Kurtosis371154.1
Mean0.19393303
Median Absolute Deviation (MAD)0
Skewness465.96586
Sum1072014.1
Variance4.2596483
MonotonicityNot monotonic
2023-02-17T19:21:14.121531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3452531
62.5%
1.010505212 838
 
< 0.1%
2.52626303 792
 
< 0.1%
5.052526061 738
 
< 0.1%
0.8420876768 711
 
< 0.1%
5.042441179 682
 
< 0.1%
1.263131515 559
 
< 0.1%
1.684175354 558
 
< 0.1%
0.7217894373 528
 
< 0.1%
7.957728546 464
 
< 0.1%
Other values (294423) 2069353
37.4%
ValueCountFrequency (%)
0 3452531
62.5%
0.007715556516 5
 
< 0.1%
0.009769187457 5
 
< 0.1%
0.009792381384 5
 
< 0.1%
0.009840825945 5
 
< 0.1%
0.009911967005 5
 
< 0.1%
0.009919848549 5
 
< 0.1%
0.009930571972 10
 
< 0.1%
0.009956403025 5
 
< 0.1%
0.009956599227 6
 
< 0.1%
ValueCountFrequency (%)
1989.432136 1
 
< 0.1%
1929.146314 1
 
< 0.1%
1253.185598 2
< 0.1%
1010.505212 2
< 0.1%
797.7672728 1
 
< 0.1%
530.5152364 2
< 0.1%
361.7149339 1
 
< 0.1%
360.8947186 3
< 0.1%
234.9144958 1
 
< 0.1%
226.0718337 1
 
< 0.1%

incidence_area
Real number (ℝ)

SKEWED  ZEROS 

Distinct7154
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012112147
Minimum0
Maximum142.1023
Zeros5519945
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:14.249828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum142.1023
Range142.1023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14039461
Coefficient of variation (CV)115.91224
Kurtosis336851.02
Mean0.0012112147
Median Absolute Deviation (MAD)0
Skewness451.99921
Sum6695.2971
Variance0.019710647
MonotonicityNot monotonic
2023-02-17T19:21:14.363085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5519945
99.9%
3.978864273 16
 
< 0.1%
7.957728546 10
 
< 0.1%
5.052526061 8
 
< 0.1%
21.22060946 6
 
< 0.1%
3.536768243 6
 
< 0.1%
3.9808547 6
 
< 0.1%
15.91545709 6
 
< 0.1%
5.305152364 5
 
< 0.1%
1.989432136 5
 
< 0.1%
Other values (7144) 7741
 
0.1%
ValueCountFrequency (%)
0 5519945
99.9%
0.009659922876 1
 
< 0.1%
0.009667593515 1
 
< 0.1%
0.009801689822 1
 
< 0.1%
0.01009112637 1
 
< 0.1%
0.01015348424 1
 
< 0.1%
0.01017956655 1
 
< 0.1%
0.01018410014 1
 
< 0.1%
0.01019055084 1
 
< 0.1%
0.01019826426 2
 
< 0.1%
ValueCountFrequency (%)
142.1022955 1
< 0.1%
117.5006061 1
< 0.1%
89.91783668 1
< 0.1%
52.94563238 1
< 0.1%
51.03561678 1
< 0.1%
50.04860721 1
< 0.1%
45.60302892 1
< 0.1%
45.47273455 1
< 0.1%
41.44650284 1
< 0.1%
39.78864273 2
< 0.1%

connection_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
4583007 
1
944747 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Length

2023-02-17T19:21:14.471026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:14.563004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Most occurring characters

ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4583007
82.9%
1 944747
 
17.1%

Severity_high
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5527157 
1
 
597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Length

2023-02-17T19:21:14.637168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:14.731779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5527157
> 99.9%
1 597
 
< 0.1%

Severity_medium
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5526660 
1
 
1094

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Length

2023-02-17T19:21:14.806728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:14.898164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5526660
> 99.9%
1 1094
 
< 0.1%

Severity_low
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5521636 
1
 
6118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

Length

2023-02-17T19:21:14.972954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.065850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5521636
99.9%
1 6118
 
0.1%

gas_natural
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
1
5428949 
0
 
98805

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Length

2023-02-17T19:21:15.141956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.233348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5428949
98.2%
0 98805
 
1.8%

Material_Acrylonitrile-Butadiene-Styrene
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5151416 
1
 
376338

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Length

2023-02-17T19:21:15.308904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.399943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5151416
93.2%
1 376338
 
6.8%

Material_Copper
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5514562 
1
 
13192

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%

Length

2023-02-17T19:21:15.476962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.571365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5514562
99.8%
1 13192
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5418775 
1
 
108979

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Length

2023-02-17T19:21:15.644915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.737082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5418775
98.0%
1 108979
 
2.0%

Material_Polyethylene
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
1
5006277 
0
521477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%

Length

2023-02-17T19:21:15.812532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:15.903849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%

Most occurring characters

ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5006277
90.6%
0 521477
 
9.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5508813 
1
 
18941

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Length

2023-02-17T19:21:15.980104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:16.071806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5508813
99.7%
1 18941
 
0.3%

Diameter2
Real number (ℝ)

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016694641
Minimum0.0001
Maximum0.37161216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:16.163606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.003969
Q10.0081
median0.0121
Q30.0256
95-th percentile0.04
Maximum0.37161216
Range0.37151216
Interquartile range (IQR)0.0175

Descriptive statistics

Standard deviation0.017866221
Coefficient of variation (CV)1.0701771
Kurtosis32.227845
Mean0.016694641
Median Absolute Deviation (MAD)0.008131
Skewness4.052524
Sum92283.866
Variance0.00031920187
MonotonicityNot monotonic
2023-02-17T19:21:16.286723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0121 1486633
26.9%
0.003969 1048452
19.0%
0.0081 1015013
18.4%
0.0256 754563
13.7%
0.04 519279
 
9.4%
0.0016 106468
 
1.9%
0.02322576 88428
 
1.6%
0.01032256 72764
 
1.3%
0.0625 67799
 
1.2%
0.04129024 63532
 
1.1%
Other values (51) 304823
 
5.5%
ValueCountFrequency (%)
0.0001 158
 
< 0.1%
0.000121 60
 
< 0.1%
0.000144 1334
 
< 0.1%
0.00016129 11
 
< 0.1%
0.000169 105
 
< 0.1%
0.000196 43
 
< 0.1%
0.000225 4246
0.1%
0.000256 1070
 
< 0.1%
0.000324 6
 
< 0.1%
0.000361 3325
0.1%
ValueCountFrequency (%)
0.37161216 67
 
< 0.1%
0.31225744 46
 
< 0.1%
0.258064 2404
 
< 0.1%
0.25 102
 
< 0.1%
0.20903184 1853
 
< 0.1%
0.16516096 7656
0.1%
0.16 561
 
< 0.1%
0.12645136 474
 
< 0.1%
0.126025 108
 
< 0.1%
0.1225 372
 
< 0.1%

Length2
Real number (ℝ)

Distinct123651
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170.8991
Minimum2.5 × 10-5
Maximum11182.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:16.419540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.5 × 10-5
5-th percentile1.004004
Q113.535041
median142.03872
Q31241.012
95-th percentile6334.2497
Maximum11182.64
Range11182.639
Interquartile range (IQR)1227.4769

Descriptive statistics

Standard deviation2136.7845
Coefficient of variation (CV)1.8249092
Kurtosis5.8325417
Mean1170.8991
Median Absolute Deviation (MAD)140.35132
Skewness2.4570189
Sum6.4724424 × 109
Variance4565848.2
MonotonicityNot monotonic
2023-02-17T19:21:16.539306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 26510
 
0.5%
1 25323
 
0.5%
0.25 18375
 
0.3%
2.25 18041
 
0.3%
1.004004 16884
 
0.3%
1.006009 10153
 
0.2%
1.002001 9546
 
0.2%
9 8694
 
0.2%
4.016016 8567
 
0.2%
1.44 8287
 
0.1%
Other values (123641) 5377374
97.3%
ValueCountFrequency (%)
2.5 × 10-514
 
< 0.1%
3.6 × 10-519
 
< 0.1%
4.9 × 10-514
 
< 0.1%
6.4 × 10-520
 
< 0.1%
8.1 × 10-519
 
< 0.1%
0.0001 61
< 0.1%
0.000121 25
< 0.1%
0.000144 12
 
< 0.1%
0.000169 31
< 0.1%
0.000196 8
 
< 0.1%
ValueCountFrequency (%)
11182.6395 8
 
< 0.1%
11182.21652 10
< 0.1%
11182.00503 20
< 0.1%
11181.58205 6
 
< 0.1%
11181.37056 18
< 0.1%
11181.15908 5
 
< 0.1%
11180.73612 11
< 0.1%
11180.52464 5
 
< 0.1%
11180.31317 5
 
< 0.1%
11180.1017 13
< 0.1%

Pressure2
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.557688
Minimum0.000625
Maximum6400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:16.643400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.000625
5-th percentile0.000625
Q10.01
median0.0225
Q316
95-th percentile256
Maximum6400
Range6399.9994
Interquartile range (IQR)15.99

Descriptive statistics

Standard deviation292.11493
Coefficient of variation (CV)6.8639754
Kurtosis214.48433
Mean42.557688
Median Absolute Deviation (MAD)0.021875
Skewness13.352417
Sum2.3524843 × 108
Variance85331.131
MonotonicityNot monotonic
2023-02-17T19:21:16.729937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16 1605762
29.0%
0.0225 1559337
28.2%
0.000625 1038331
18.8%
0.01 562184
 
10.2%
256 251041
 
4.5%
25 159352
 
2.9%
0.16 143799
 
2.6%
2.89 112953
 
2.0%
2450.25 29224
 
0.5%
0.0025 19994
 
0.4%
Other values (10) 45777
 
0.8%
ValueCountFrequency (%)
0.000625 1038331
18.8%
0.0025 19994
 
0.4%
0.01 562184
 
10.2%
0.0225 1559337
28.2%
0.16 143799
 
2.6%
2.89 112953
 
2.0%
4 11816
 
0.2%
16 1605762
29.0%
25 159352
 
2.9%
100 6189
 
0.1%
ValueCountFrequency (%)
6400 2391
 
< 0.1%
5184 4851
 
0.1%
3540.25 2784
 
0.1%
2450.25 29224
 
0.5%
2025 2085
 
< 0.1%
1600 667
 
< 0.1%
1296 9169
 
0.2%
625 677
 
< 0.1%
256 251041
4.5%
144 5148
 
0.1%

Province
Categorical

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.1 MiB
barcelona
1520717 
valencia
562995 
madrid
520803 
girona
315159 
tarragona
310243 
Other values (33)
2297837 

Length

Max length11
Median length10
Mean length7.8892478
Min length4

Characters and Unicode

Total characters43609821
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowciudad real
2nd rowciudad real
3rd rowciudad real
4th rowciudad real
5th rowciudad real

Common Values

ValueCountFrequency (%)
barcelona 1520717
27.5%
valencia 562995
 
10.2%
madrid 520803
 
9.4%
girona 315159
 
5.7%
tarragona 310243
 
5.6%
alicante 240594
 
4.4%
la coruna 161931
 
2.9%
sevilla 150050
 
2.7%
pontevedra 147680
 
2.7%
valladolid 146228
 
2.6%
Other values (28) 1451354
26.3%

Length

2023-02-17T19:21:16.834991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
barcelona 1520717
25.9%
valencia 562995
 
9.6%
madrid 520803
 
8.9%
girona 315159
 
5.4%
tarragona 310243
 
5.3%
alicante 240594
 
4.1%
la 239413
 
4.1%
coruna 161931
 
2.8%
sevilla 150050
 
2.6%
pontevedra 147680
 
2.5%
Other values (30) 1695777
28.9%

Most occurring characters

ValueCountFrequency (%)
a 9520063
21.8%
l 4531042
10.4%
r 3835397
8.8%
n 3703267
 
8.5%
e 3670776
 
8.4%
o 3532012
 
8.1%
c 3008379
 
6.9%
i 2360352
 
5.4%
d 2185128
 
5.0%
b 1717608
 
3.9%
Other values (11) 5545797
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43272213
99.2%
Space Separator 337608
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9520063
22.0%
l 4531042
10.5%
r 3835397
8.9%
n 3703267
 
8.6%
e 3670776
 
8.5%
o 3532012
 
8.2%
c 3008379
 
7.0%
i 2360352
 
5.5%
d 2185128
 
5.0%
b 1717608
 
4.0%
Other values (10) 5208189
12.0%
Space Separator
ValueCountFrequency (%)
337608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43272213
99.2%
Common 337608
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9520063
22.0%
l 4531042
10.5%
r 3835397
8.9%
n 3703267
 
8.6%
e 3670776
 
8.5%
o 3532012
 
8.2%
c 3008379
 
7.0%
i 2360352
 
5.5%
d 2185128
 
5.0%
b 1717608
 
4.0%
Other values (10) 5208189
12.0%
Common
ValueCountFrequency (%)
337608
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43609821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9520063
21.8%
l 4531042
10.4%
r 3835397
8.8%
n 3703267
 
8.5%
e 3670776
 
8.4%
o 3532012
 
8.1%
c 3008379
 
6.9%
i 2360352
 
5.4%
d 2185128
 
5.0%
b 1717608
 
3.9%
Other values (11) 5545797
12.7%

Town
Categorical

Distinct1884
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.2 MiB
madrid
 
352803
barcelona
 
220382
valencia
 
91691
sevilla
 
84752
terrassa
 
66997
Other values (1879)
4711129 

Length

Max length25
Median length22
Mean length10.386721
Min length3

Characters and Unicode

Total characters57415236
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowtorralba de calatrava
2nd rowtorralba de calatrava
3rd rowtorralba de calatrava
4th rowtorralba de calatrava
5th rowtorralba de calatrava

Common Values

ValueCountFrequency (%)
madrid 352803
 
6.4%
barcelona 220382
 
4.0%
valencia 91691
 
1.7%
sevilla 84752
 
1.5%
terrassa 66997
 
1.2%
sabadell 64005
 
1.2%
alicante/alacant 63025
 
1.1%
vigo 55248
 
1.0%
malaga 55163
 
1.0%
cordoba 51296
 
0.9%
Other values (1874) 4422392
80.0%

Length

2023-02-17T19:21:16.944958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 827915
 
9.5%
madrid 352803
 
4.0%
del 297452
 
3.4%
sant 241253
 
2.8%
barcelona 220382
 
2.5%
la 214895
 
2.5%
valles 128361
 
1.5%
llobregat 101428
 
1.2%
valencia 93930
 
1.1%
sevilla 84752
 
1.0%
Other values (2046) 6194480
70.7%

Most occurring characters

ValueCountFrequency (%)
a 9652351
16.8%
e 5745086
10.0%
l 5624238
9.8%
r 4294019
 
7.5%
o 3425474
 
6.0%
3229924
 
5.6%
d 3156758
 
5.5%
n 3123478
 
5.4%
s 2955643
 
5.1%
i 2821441
 
4.9%
Other values (19) 13386824
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53890741
93.9%
Space Separator 3229924
 
5.6%
Other Punctuation 207528
 
0.4%
Dash Punctuation 87031
 
0.2%
Decimal Number 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9652351
17.9%
e 5745086
10.7%
l 5624238
10.4%
r 4294019
 
8.0%
o 3425474
 
6.4%
d 3156758
 
5.9%
n 3123478
 
5.8%
s 2955643
 
5.5%
i 2821441
 
5.2%
t 2323037
 
4.3%
Other values (14) 10769216
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 202734
97.7%
. 4794
 
2.3%
Space Separator
ValueCountFrequency (%)
3229924
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87031
100.0%
Decimal Number
ValueCountFrequency (%)
7 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53890741
93.9%
Common 3524495
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9652351
17.9%
e 5745086
10.7%
l 5624238
10.4%
r 4294019
 
8.0%
o 3425474
 
6.4%
d 3156758
 
5.9%
n 3123478
 
5.8%
s 2955643
 
5.5%
i 2821441
 
5.2%
t 2323037
 
4.3%
Other values (14) 10769216
20.0%
Common
ValueCountFrequency (%)
3229924
91.6%
/ 202734
 
5.8%
- 87031
 
2.5%
. 4794
 
0.1%
7 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57415236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9652351
16.8%
e 5745086
10.0%
l 5624238
9.8%
r 4294019
 
7.5%
o 3425474
 
6.0%
3229924
 
5.6%
d 3156758
 
5.5%
n 3123478
 
5.4%
s 2955643
 
5.1%
i 2821441
 
4.9%
Other values (19) 13386824
23.3%

TownCount
Real number (ℝ)

Distinct1188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56625.732
Minimum1
Maximum401156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.2 MiB
2023-02-17T19:21:17.064386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1399
Q15706
median12897
Q341902
95-th percentile401156
Maximum401156
Range401155
Interquartile range (IQR)36196

Descriptive statistics

Standard deviation107032.55
Coefficient of variation (CV)1.8901752
Kurtosis4.7882447
Mean56625.732
Median Absolute Deviation (MAD)9392
Skewness2.485763
Sum3.1301312 × 1011
Variance1.1455968 × 1010
MonotonicityNot monotonic
2023-02-17T19:21:17.185184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401156 352803
 
6.4%
290182 220382
 
4.0%
124155 91691
 
1.7%
100982 84752
 
1.5%
77985 66997
 
1.2%
75580 64005
 
1.2%
68130 63025
 
1.1%
61439 55248
 
1.0%
72471 55163
 
1.0%
56505 51296
 
0.9%
Other values (1178) 4422392
80.0%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 126
 
< 0.1%
3 19
 
< 0.1%
4 115
 
< 0.1%
5 32
 
< 0.1%
6 275
< 0.1%
7 48
 
< 0.1%
8 227
< 0.1%
9 74
 
< 0.1%
10 341
< 0.1%
ValueCountFrequency (%)
401156 352803
6.4%
290182 220382
4.0%
124155 91691
 
1.7%
100982 84752
 
1.5%
77985 66997
 
1.2%
75580 64005
 
1.2%
72471 55163
 
1.0%
68130 63025
 
1.1%
63033 47690
 
0.9%
61439 55248
 
1.0%

Autonomía_Andalucía
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5035580 
1
 
492174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Length

2023-02-17T19:21:17.302027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:17.396514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5035580
91.1%
1 492174
 
8.9%

Autonomía_Aragón
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5503694 
1
 
24060

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%

Length

2023-02-17T19:21:17.473526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:17.563866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5503694
99.6%
1 24060
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5521127 
1
 
6627

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Length

2023-02-17T19:21:17.636022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:17.726159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5521127
99.9%
1 6627
 
0.1%

Autonomía_Castilla y León
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5060339 
1
 
467415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Length

2023-02-17T19:21:17.801104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:17.894725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5060339
91.5%
1 467415
 
8.5%

Autonomía_Castilla-La Mancha
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5161077 
1
 
366677

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%

Length

2023-02-17T19:21:17.967682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.062055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5161077
93.4%
1 366677
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
3222424 
1
2305330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%

Length

2023-02-17T19:21:18.137852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.229835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%

Most occurring characters

ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3222424
58.3%
1 2305330
41.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
4634278 
1
893476 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%

Length

2023-02-17T19:21:18.306010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.397873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4634278
83.8%
1 893476
 
16.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5522325 
1
 
5429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Length

2023-02-17T19:21:18.475192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.565544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5522325
99.9%
1 5429
 
0.1%

Autonomía_Galicia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5154667 
1
 
373087

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Length

2023-02-17T19:21:18.638945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.729825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5154667
93.3%
1 373087
 
6.7%

Autonomía_Madrid (Comunidad de)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5012380 
1
515374 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%

Length

2023-02-17T19:21:18.803611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:18.899762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5012380
90.7%
1 515374
 
9.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5527131 
1
 
623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Length

2023-02-17T19:21:18.974264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:19.067617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5527131
> 99.9%
1 623
 
< 0.1%

Autonomía_Rioja (La)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5450272 
1
 
77482

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Length

2023-02-17T19:21:19.143827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:19.236977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5450272
98.6%
1 77482
 
1.4%

Madrid
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.8 MiB
0
5174951 
1
 
352803

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5527754
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Length

2023-02-17T19:21:19.311057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-17T19:21:19.400165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5527754
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5527754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5527754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5174951
93.6%
1 352803
 
6.4%

Interactions

2023-02-17T19:20:01.367630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:26.592056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:49.731229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:13.199725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:35.710052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:57.122029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:18.939105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:40.999800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:02.576848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:24.674304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:46.457059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:11.011889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:33.767133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:56.290934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:18.525458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:41.242918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:03.769347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:26.437759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:48.700953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:11.497857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:33.924782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:56.234817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:18.473902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:40.125217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:02.392379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:27.557773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:50.694967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:14.159848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:36.606872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:58.036858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:19.845286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:41.869443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:03.668139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:25.614955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:47.368185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:11.893726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:34.730059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:57.235234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:19.433313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:42.169970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:04.713193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:27.357019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:49.645266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:12.423449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:34.854337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:57.169189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:19.381842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:41.016428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:03.661295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:28.547714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:51.654857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:15.083893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:37.477815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:58.918000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:20.774921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:42.776015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:04.573944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:26.511283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:48.287109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:12.809109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:35.638164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:58.171711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:20.351064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:43.148138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:05.607848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:28.289802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:50.574134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:13.342928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:35.763214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:58.185894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:20.278913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:41.862160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:04.730943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:29.499015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:52.579789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:16.041178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:38.321296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:59.861158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:21.693744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:43.651220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:05.480883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:27.425194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:49.285067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:13.706832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:36.589489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:59.073105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:21.267919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:44.065749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:06.573420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:29.209168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:51.502555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:14.263889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:36.719259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:59.138193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:21.168052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:42.751410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:05.658161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:30.461151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:53.528131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:17.007830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:39.233947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:00.745798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:22.585737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:44.549980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:06.395671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:28.336057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:50.293121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:14.604093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:37.571171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:00.009275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:22.199981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:44.979967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:07.529946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:30.125453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:52.420813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:15.197635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:37.631241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:00.093844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:22.102126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:43.624227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:06.645297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:31.434984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:54.503939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:17.993142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:40.125935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:01.617749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:23.473314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:45.448331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:07.319724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:29.224413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:51.254962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:15.540857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:38.476261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:00.943160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:23.101440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:45.909084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:08.495041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:31.089993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:53.350582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-17T19:17:32.005049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-17T19:18:25.522449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:47.928964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:10.226221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:32.161102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:53.305207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:17.410704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:42.000167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:05.481995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:28.244841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:49.969541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:11.545075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:33.654840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:55.294085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:17.341742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:39.141279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-02-17T19:11:42.979986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:06.417602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:29.140962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:50.845904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:12.447028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:34.575023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:56.200812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:18.242085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:40.043245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:03.826029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:26.939142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:49.750188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:12.051953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:34.643064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:57.061110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:19.817772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:42.058244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:04.599187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:27.422182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:49.773209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:12.078193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:33.977740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:55.077071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:19.231062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:43.959015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:07.396263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:30.067176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:51.755475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:13.514186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:35.486202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:57.111408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:19.151908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:40.967155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:04.909736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:27.961370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:50.726752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:12.977767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:35.556856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:57.995889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:20.733985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:42.979664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:05.511891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:28.325449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:50.701860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:12.982785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:34.889178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:55.958027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:20.177962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:44.915906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:08.348116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:31.124153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:52.652071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:14.417633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:36.415143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:58.035126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:20.077314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:41.875161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:06.028180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:28.874075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:51.667835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:13.889864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:36.495532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:58.940149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:21.709064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:43.890915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:06.445190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:29.249006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:51.597602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:13.939597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:35.756296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:56.835944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:21.071123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:45.870909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:09.295067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:32.065802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:53.546907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:15.329803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:37.354302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:58.912965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:20.974967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:42.802342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:07.007180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:29.878245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:52.575878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:14.828053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:37.421030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:59.892196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:22.671128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:44.835848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:07.771898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:30.180147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:52.497963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:14.824929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:36.664115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:57.726837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:21.998147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:46.809889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:10.252498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:32.994600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:54.441914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:16.218073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:38.273178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:59.823191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:21.900125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:43.703207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:08.303090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:30.849693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:53.505789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:15.780207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:38.372125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:00.844828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:23.634993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:45.801341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:08.720900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:31.131138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:53.434890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:15.777487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:37.500021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:58.629767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:22.859699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:47.775194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:11.205862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:33.876510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:55.341195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:17.125013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:39.194927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:00.737400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:22.831994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:44.634931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:09.184726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:31.859276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:54.436172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:16.691352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:39.330098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:01.763064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:24.599747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:46.724774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:09.648121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:32.048902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:54.352875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:16.685499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:38.370193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:59.468193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:23.786321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:11:48.740829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:12.211045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:34.810098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:12:56.236154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:18.007357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:13:40.106803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:01.635882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:23.756995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:14:45.572069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:10.060977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:32.850062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:15:55.348135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:17.622745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:16:40.263774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:02.785818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:25.506899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:17:47.697782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:10.561231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:33.002715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:18:55.264458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:17.582885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:19:39.257142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-17T19:20:00.349905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-17T19:21:19.548105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
PipeIdNo_InspectionsInspectionYearProbability_ratepreventive_maintenance_rateAge_pipe_at_inspectionAverage_MonthsLastRevMonthsLastRevrelative_riskaverage_severity_pipeYearBuiltDiameterLengthPressureNumConnectionsaspectRelative_Thicknesspipe_areaarea_connectionincidence_areaDiameter2Length2Pressure2TownCountNo_Incidentspipe_inspected_frequentlyIncidenceNumConnectionsUnderconnection_boolSeverity_highSeverity_mediumSeverity_lowgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_PolypropyleneProvinceAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Madrid
PipeId1.0000.244-0.115-0.022-0.022-0.2230.0780.078-0.0220.0220.2430.114-0.0120.043-0.060-0.0050.0070.024-0.067-0.0170.114-0.0120.043-0.0500.0210.1410.0270.0070.0980.0050.0080.0270.2910.0420.1030.0830.0520.1640.3260.2510.0280.0280.4390.3320.5400.1750.0700.3260.3330.0190.1850.272
No_Inspections0.2441.000-0.1600.0130.0130.316-0.212-0.1420.013-0.013-0.3620.1530.118-0.1120.052-0.1860.1390.1670.029-0.0100.1530.118-0.1120.2450.0180.1550.0320.0110.0660.0040.0060.0330.5180.0720.1580.0630.0790.2440.1500.0820.0900.0620.1140.1300.1700.1070.0680.0970.2660.0430.0470.260
InspectionYear-0.115-0.1601.0000.0050.0050.253-0.0320.2240.005-0.0050.132-0.0560.0200.0310.0100.029-0.043-0.0000.015-0.000-0.0560.0200.031-0.0610.0030.0580.0060.0030.0110.0030.0030.0070.1660.0370.0510.0170.0320.0810.0460.0310.0350.0280.0140.0210.0320.0220.0360.0420.0170.0170.0310.022
Probability_rate-0.0220.0130.0051.0001.0000.038-0.034-0.0371.000-1.000-0.0390.0030.053-0.0290.083-0.0370.0220.0480.0640.4950.0030.053-0.0290.0430.6970.0110.5720.0000.0870.1420.1930.5300.1160.0020.0700.0460.0270.0080.0340.0090.0040.0020.0150.0130.0120.0200.0410.0180.0880.0070.0070.093
preventive_maintenance_rate-0.0220.0130.0051.0001.0000.038-0.034-0.0371.000-1.000-0.0390.0030.053-0.0290.083-0.0370.0220.0480.0640.4950.0030.053-0.0290.0430.6260.0110.5690.0000.0870.2080.2630.5510.1180.0010.0700.0490.0280.0080.0320.0090.0050.0030.0150.0130.0160.0200.0390.0180.0890.0070.0070.095
Age_pipe_at_inspection-0.2230.3160.2530.0380.0381.0000.1860.1530.038-0.038-0.9150.1030.035-0.2470.075-0.1940.2160.0700.0520.0050.1030.035-0.2470.3530.0310.0740.0300.0070.1060.0070.0140.0270.2690.0540.1020.2420.1080.1620.1330.1140.0380.0200.0390.1130.2320.1450.0710.1110.1600.0260.0130.189
Average_MonthsLastRev0.078-0.212-0.032-0.034-0.0340.1861.0000.560-0.0340.034-0.1890.040-0.058-0.101-0.020-0.0510.089-0.044-0.022-0.0180.040-0.058-0.101-0.0200.0180.4030.0310.0040.0410.0080.0100.0300.1160.0290.0740.0250.0660.1770.1850.0340.0280.0410.0970.2720.3210.0060.1160.0610.3860.0670.0520.315
MonthsLastRev0.078-0.1420.224-0.037-0.0370.1530.5601.000-0.0370.037-0.0540.027-0.026-0.073-0.009-0.0550.067-0.018-0.013-0.0220.027-0.026-0.073-0.0850.0200.2940.0350.0040.0260.0100.0150.0320.1180.0290.0610.0460.0510.0990.1920.0990.0380.0340.0630.2130.2670.0870.0660.0930.4500.0520.0580.373
relative_risk-0.0220.0130.0051.0001.0000.038-0.034-0.0371.000-1.000-0.0400.0030.053-0.0290.083-0.0370.0220.0480.0640.4950.0030.053-0.0290.0430.9970.0090.5090.0000.0870.1790.2410.4530.0420.0010.0330.0440.0260.0080.0300.0070.0040.0020.0150.0090.0110.0200.0140.0140.0860.0040.0070.089
average_severity_pipe0.022-0.013-0.005-1.000-1.000-0.0380.0340.037-1.0001.0000.039-0.003-0.0530.029-0.0830.037-0.022-0.048-0.064-0.495-0.003-0.0530.029-0.0430.3480.0080.4570.0000.0560.4110.3610.3390.0730.0040.0440.0380.0230.0110.0180.0060.0030.0000.0100.0070.0050.0160.0110.0060.0440.0040.0050.037
YearBuilt0.243-0.3620.132-0.039-0.039-0.915-0.189-0.054-0.0400.0391.000-0.117-0.0290.270-0.0780.211-0.239-0.069-0.053-0.007-0.117-0.0290.270-0.3830.0380.0910.0400.0150.1200.0100.0160.0370.3760.0990.1380.2730.1560.4210.1670.1310.0850.0350.0520.1240.2520.1650.1070.1340.2080.0310.0260.244
Diameter0.1140.153-0.0560.0030.0030.1030.0400.0270.003-0.003-0.1171.0000.013-0.352-0.177-0.4470.6050.292-0.269-0.0071.0000.013-0.3520.2160.0190.0340.0200.0040.0920.0090.0070.0190.2150.2770.0890.2890.3510.0320.1550.0970.1440.0220.2020.0990.1030.1730.0340.0450.2310.0110.0540.282
Length-0.0120.1180.0200.0530.0530.035-0.058-0.0260.053-0.053-0.0290.0131.0000.0040.532-0.494-0.0050.9540.3810.0230.0131.0000.004-0.0080.0310.0450.0290.0050.5020.0090.0100.0260.0130.0150.0040.0250.0220.0060.0280.0120.0060.0080.0180.0160.0310.0420.0110.0130.0100.0030.0120.008
Pressure0.043-0.1120.031-0.029-0.029-0.247-0.101-0.073-0.0290.0290.270-0.3520.0041.000-0.1320.830-0.942-0.086-0.089-0.009-0.3520.0041.000-0.2850.0050.0410.0050.0020.1110.0020.0010.0040.0330.8900.0120.0350.7450.0140.1010.0310.1790.0180.0350.0840.0590.0690.0080.0450.0500.0030.0460.053
NumConnections-0.0600.0520.0100.0830.0830.075-0.020-0.0090.083-0.083-0.078-0.1770.532-0.1321.000-0.3340.0540.4510.9430.038-0.1770.532-0.1320.0420.0510.0030.0470.0110.5180.0130.0160.0430.0300.0620.0180.0140.0470.0090.0230.0250.0060.0070.0170.0070.0420.0210.0080.0130.0070.0000.0040.008
aspect-0.005-0.1860.029-0.037-0.037-0.194-0.051-0.055-0.0370.0370.211-0.447-0.4940.830-0.3341.000-0.840-0.593-0.213-0.011-0.447-0.4940.830-0.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0050.0000.0020.0000.0020.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.000
Relative_Thickness0.0070.139-0.0430.0220.0220.2160.0890.0670.022-0.022-0.2390.605-0.005-0.9420.054-0.8401.0000.158-0.0130.0040.605-0.005-0.9420.2960.0340.0870.0250.0020.0990.0150.0120.0190.0650.1260.0240.4390.1690.0180.1760.0660.0320.0160.1440.1290.2290.1340.0150.1300.3240.0050.0580.421
pipe_area0.0240.167-0.0000.0480.0480.070-0.044-0.0180.048-0.048-0.0690.2920.954-0.0860.451-0.5930.1581.0000.2820.0180.2920.954-0.0860.0600.0320.0250.0240.0010.2900.0110.0090.0200.0460.1020.0200.0420.1020.0110.0370.0300.0340.0020.0350.0250.0240.0390.0060.0110.0610.0030.0150.084
area_connection-0.0670.0290.0150.0640.0640.052-0.022-0.0130.064-0.064-0.053-0.2690.381-0.0890.943-0.213-0.0130.2821.0000.031-0.2690.381-0.0890.0240.0000.0020.0000.0000.0000.0000.0000.0000.0030.0020.0110.0000.0020.0000.0030.0000.0000.0000.0020.0020.0010.0000.0000.0020.0000.0000.0000.000
incidence_area-0.017-0.010-0.0000.4950.4950.005-0.018-0.0220.495-0.495-0.007-0.0070.023-0.0090.038-0.0110.0040.0180.0311.000-0.0070.023-0.0090.0110.0260.0000.0970.0000.0000.0000.0040.1080.0150.0000.0280.0000.0050.0020.0020.0020.0000.0000.0010.0000.0020.0000.0080.0010.0030.0000.0000.000
Diameter20.1140.153-0.0560.0030.0030.1030.0400.0270.003-0.003-0.1171.0000.013-0.352-0.177-0.4470.6050.292-0.269-0.0071.0000.013-0.3520.2160.0130.0190.0080.0010.0760.0070.0040.0050.0520.2280.0190.1950.2620.0190.1050.0370.1400.0070.0330.0410.0680.0440.0100.0270.1940.0040.0350.253
Length2-0.0120.1180.0200.0530.0530.035-0.058-0.0260.053-0.053-0.0290.0131.0000.0040.532-0.494-0.0050.9540.3810.0230.0131.0000.004-0.0080.0300.0320.0280.0050.4660.0090.0090.0250.0110.0130.0020.0220.0200.0030.0230.0090.0050.0050.0100.0160.0290.0340.0080.0060.0090.0030.0070.008
Pressure20.043-0.1120.031-0.029-0.029-0.247-0.101-0.073-0.0290.0290.270-0.3520.0041.000-0.1320.830-0.942-0.086-0.089-0.009-0.3520.0041.000-0.2850.0030.0330.0030.0000.0440.0000.0010.0030.0130.3580.0050.0140.2990.0060.0870.0280.0790.0020.0280.0830.0550.0640.0030.0340.0300.0000.0080.022
TownCount-0.0500.245-0.0610.0430.0430.353-0.020-0.0850.043-0.043-0.3830.216-0.008-0.2850.042-0.2230.2960.0600.0240.0110.216-0.008-0.2851.0000.0450.1260.0350.0020.0500.0150.0120.0310.0770.0560.0260.2980.1480.0190.5800.4460.0390.0200.1260.1570.3560.3310.0180.1420.8160.0060.0701.000
No_Incidents0.0210.0180.0030.6970.6260.0310.0180.0200.9970.3480.0380.0190.0310.0050.0510.0000.0340.0320.0000.0260.0130.0300.0030.0451.0000.0090.5050.0000.0870.1370.1860.4510.0370.0000.0300.0440.0260.0020.0450.0070.0020.0020.0150.0100.0100.0200.0140.0140.0860.0020.0070.088
pipe_inspected_frequently0.1410.1550.0580.0110.0110.0740.4030.2940.0090.0080.0910.0340.0450.0410.0030.0000.0870.0250.0020.0000.0190.0320.0330.1260.0091.0000.0050.0010.0070.0010.0010.0050.0380.0270.0130.0120.0360.0180.2210.0280.0200.0120.0240.0960.1410.0010.0120.0530.1050.0040.0120.082
Incidence0.0270.0320.0060.5720.5690.0300.0310.0350.5090.4570.0400.0200.0290.0050.0470.0000.0250.0240.0000.0970.0080.0280.0030.0350.5050.0051.0000.0000.0410.2760.3740.8850.0430.0000.0290.0180.0140.0010.0400.0020.0000.0010.0070.0030.0050.0100.0100.0050.0380.0020.0030.034
NumConnectionsUnder0.0070.0110.0030.0000.0000.0070.0040.0040.0000.0000.0150.0040.0050.0020.0110.0000.0020.0010.0000.0000.0010.0050.0000.0020.0000.0010.0001.0000.0070.0000.0000.0000.0030.0030.0080.0000.0010.0120.0090.0030.0010.0000.0000.0010.0040.0040.0000.0000.0020.0000.0030.001
connection_bool0.0980.0660.0110.0870.0870.1060.0410.0260.0870.0560.1200.0920.5020.1110.5180.0000.0990.2900.0000.0000.0760.4660.0440.0500.0870.0070.0410.0071.0000.0110.0160.0360.0280.1160.0130.0010.0990.0110.0780.0260.0130.0080.0210.0110.0460.0020.0020.0170.0070.0020.0010.004
Severity_high0.0050.0040.0030.1420.2080.0070.0080.0100.1790.4110.0100.0090.0090.0020.0130.0000.0150.0110.0000.0000.0070.0090.0000.0150.1370.0010.2760.0000.0111.0000.0000.0000.0010.0010.0030.0160.0070.0010.0120.0010.0000.0000.0010.0020.0010.0030.0000.0020.0110.0000.0010.014
Severity_medium0.0080.0060.0030.1930.2630.0140.0100.0150.2410.3610.0160.0070.0100.0010.0160.0000.0120.0090.0000.0040.0040.0090.0010.0120.1860.0010.3740.0000.0160.0001.0000.0000.0020.0010.0000.0130.0060.0020.0140.0040.0000.0000.0030.0030.0090.0060.0000.0040.0060.0000.0010.008
Severity_low0.0270.0330.0070.5300.5510.0270.0300.0320.4530.3390.0370.0190.0260.0040.0430.0000.0190.0200.0000.1080.0050.0250.0030.0310.4510.0050.8850.0000.0360.0000.0001.0000.0480.0010.0310.0100.0120.0000.0390.0010.0000.0010.0060.0010.0100.0080.0120.0030.0370.0020.0030.031
gas_natural0.2910.5180.1660.1160.1180.2690.1160.1180.0420.0730.3760.2150.0130.0330.0300.0000.0650.0460.0030.0150.0520.0110.0130.0770.0370.0380.0430.0030.0280.0010.0020.0481.0000.0230.2600.0190.0310.0010.1830.0080.0660.0050.0100.0210.0170.0370.0410.0300.0150.0100.0120.035
Material_Acrylonitrile-Butadiene-Styrene0.0420.0720.0370.0020.0010.0540.0290.0290.0010.0040.0990.2770.0150.8900.0620.0020.1260.1020.0020.0000.2280.0130.3580.0560.0000.0270.0000.0030.1160.0010.0010.0010.0231.0000.0130.0380.8370.0160.1780.0250.1440.0080.0380.0150.0070.0070.0070.0040.0420.0020.0130.025
Material_Copper0.1030.1580.0510.0700.0700.1020.0740.0610.0330.0440.1380.0890.0040.0120.0180.0050.0240.0200.0110.0280.0190.0020.0050.0260.0300.0130.0290.0080.0130.0030.0000.0310.2600.0131.0000.0070.1520.0030.0710.0100.0030.0020.0070.0280.0310.0100.0580.0100.0120.0020.0050.013
Material_Fiberglass-Reinforced Plastic0.0830.0630.0170.0460.0490.2420.0250.0460.0440.0380.2730.2890.0250.0350.0140.0000.4390.0420.0000.0000.1950.0220.0140.2980.0440.0120.0180.0000.0010.0160.0130.0100.0190.0380.0071.0000.4390.0080.1780.0240.0090.0050.0430.0380.0450.0590.0040.0380.1400.0010.0170.183
Material_Polyethylene0.0520.0790.0320.0270.0280.1080.0660.0510.0260.0230.1560.3510.0220.7450.0470.0020.1690.1020.0020.0050.2620.0200.2990.1480.0260.0360.0140.0010.0990.0070.0060.0120.0310.8370.1520.4391.0000.1820.1910.0200.1200.0100.0530.0250.0180.0400.0010.0090.1020.0020.0200.103
Material_Polypropylene0.1640.2440.0810.0080.0080.1620.1770.0990.0080.0110.4210.0320.0060.0140.0090.0000.0180.0110.0000.0020.0190.0030.0060.0190.0020.0180.0010.0120.0110.0010.0020.0000.0010.0160.0030.0080.1821.0000.0710.0560.0020.0010.0080.0070.0180.0160.0000.0130.0110.0050.0010.012
Province0.3260.1500.0460.0340.0320.1330.1850.1920.0300.0180.1670.1550.0280.1010.0230.0020.1760.0370.0030.0020.1050.0230.0870.5800.0450.2210.0400.0090.0780.0120.0140.0390.1830.1780.0710.1780.1910.0711.0000.9991.0000.1031.0001.0000.9920.9810.0970.9990.9940.0611.0000.810
Autonomía_Andalucía0.2510.0820.0310.0090.0090.1140.0340.0990.0070.0060.1310.0970.0120.0310.0250.0000.0660.0300.0000.0020.0370.0090.0280.4460.0070.0280.0020.0030.0260.0010.0040.0010.0080.0250.0100.0240.0200.0560.9991.0000.0210.0110.0950.0830.2640.1370.0100.0840.1000.0030.0370.082
Autonomía_Aragón0.0280.0900.0350.0040.0050.0380.0280.0380.0040.0030.0850.1440.0060.1790.0060.0000.0320.0340.0000.0000.1400.0050.0790.0390.0020.0200.0000.0010.0130.0000.0000.0000.0660.1440.0030.0090.1200.0021.0000.0211.0000.0020.0200.0180.0560.0290.0020.0180.0210.0000.0080.017
Autonomía_Balears (Illes)0.0280.0620.0280.0020.0030.0200.0410.0340.0020.0000.0350.0220.0080.0180.0070.0000.0160.0020.0000.0000.0070.0050.0020.0200.0020.0120.0010.0000.0080.0000.0000.0010.0050.0080.0020.0050.0100.0010.1030.0110.0021.0000.0110.0090.0290.0150.0010.0090.0110.0000.0040.009
Autonomía_Castilla y León0.4390.1140.0140.0150.0150.0390.0970.0630.0150.0100.0520.2020.0180.0350.0170.0000.1440.0350.0020.0010.0330.0100.0280.1260.0150.0240.0070.0000.0210.0010.0030.0060.0100.0380.0070.0430.0530.0081.0000.0950.0200.0111.0000.0810.2570.1330.0100.0820.0970.0030.0360.079
Autonomía_Castilla-La Mancha0.3320.1300.0210.0130.0130.1130.2720.2130.0090.0070.1240.0990.0160.0840.0070.0000.1290.0250.0020.0000.0410.0160.0830.1570.0100.0960.0030.0010.0110.0020.0030.0010.0210.0150.0280.0380.0250.0071.0000.0830.0180.0090.0811.0000.2250.1170.0080.0720.0850.0030.0320.070
Autonomía_Cataluña0.5400.1700.0320.0120.0160.2320.3210.2670.0110.0050.2520.1030.0310.0590.0420.0010.2290.0240.0010.0020.0680.0290.0550.3560.0100.1410.0050.0040.0460.0010.0090.0100.0170.0070.0310.0450.0180.0180.9920.2640.0560.0290.2570.2251.0000.3710.0270.2280.2710.0090.1010.221
Autonomía_Comunidad Valenciana0.1750.1070.0220.0200.0200.1450.0060.0870.0200.0160.1650.1730.0420.0690.0210.0000.1340.0390.0000.0000.0440.0340.0640.3310.0200.0010.0100.0040.0020.0030.0060.0080.0370.0070.0100.0590.0400.0160.9810.1370.0290.0150.1330.1170.3711.0000.0140.1180.1410.0050.0520.115
Autonomía_Extremadura0.0700.0680.0360.0410.0390.0710.1160.0660.0140.0110.1070.0340.0110.0080.0080.0000.0150.0060.0000.0080.0100.0080.0030.0180.0140.0120.0100.0000.0020.0000.0000.0120.0410.0070.0580.0040.0010.0000.0970.0100.0020.0010.0100.0080.0270.0141.0000.0080.0100.0000.0040.008
Autonomía_Galicia0.3260.0970.0420.0180.0180.1110.0610.0930.0140.0060.1340.0450.0130.0450.0130.0000.1300.0110.0020.0010.0270.0060.0340.1420.0140.0530.0050.0000.0170.0020.0040.0030.0300.0040.0100.0380.0090.0130.9990.0840.0180.0090.0820.0720.2280.1180.0081.0000.0860.0030.0320.070
Autonomía_Madrid (Comunidad de)0.3330.2660.0170.0880.0890.1600.3860.4500.0860.0440.2080.2310.0100.0500.0070.0000.3240.0610.0000.0030.1940.0090.0300.8160.0860.1050.0380.0020.0070.0110.0060.0370.0150.0420.0120.1400.1020.0110.9940.1000.0210.0110.0970.0850.2710.1410.0100.0861.0000.0030.0380.814
Autonomía_Navarra (Comunidad Foral de)0.0190.0430.0170.0070.0070.0260.0670.0520.0040.0040.0310.0110.0030.0030.0000.0000.0050.0030.0000.0000.0040.0030.0000.0060.0020.0040.0020.0000.0020.0000.0000.0020.0100.0020.0020.0010.0020.0050.0610.0030.0000.0000.0030.0030.0090.0050.0000.0030.0031.0000.0010.003
Autonomía_Rioja (La)0.1850.0470.0310.0070.0070.0130.0520.0580.0070.0050.0260.0540.0120.0460.0040.0000.0580.0150.0000.0000.0350.0070.0080.0700.0070.0120.0030.0030.0010.0010.0010.0030.0120.0130.0050.0170.0200.0011.0000.0370.0080.0040.0360.0320.1010.0520.0040.0320.0380.0011.0000.031
Madrid0.2720.2600.0220.0930.0950.1890.3150.3730.0890.0370.2440.2820.0080.0530.0080.0000.4210.0840.0000.0000.2530.0080.0221.0000.0880.0820.0340.0010.0040.0140.0080.0310.0350.0250.0130.1830.1030.0120.8100.0820.0170.0090.0790.0700.2210.1150.0080.0700.8140.0030.0311.000

Missing values

2023-02-17T19:20:26.205984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-17T19:20:38.308917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PipeIdNo_InspectionsNo_IncidentsInspectionYearProbability_ratepreventive_maintenance_rateAge_pipe_at_inspectionpipe_inspected_frequentlyAverage_MonthsLastRevMonthsLastRevrelative_riskaverage_severity_pipeIncidenceYearBuiltDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridleaspectRelative_Thicknesspipe_areaarea_connectionincidence_areaconnection_boolSeverity_highSeverity_mediumSeverity_lowgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_PolypropyleneDiameter2Length2Pressure2ProvinceTownTownCountAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Madrid
048961650.002013.000.000.001.00121.4016.000.004.000.0020120.067.790.400000.810.161.540.000.0000001000100.0060.720.16ciudad realtorralba de calatrava20930000100000000
148961650.002015.000.000.003.00121.4022.000.004.000.0020120.067.790.400000.810.161.540.000.0000001000100.0060.720.16ciudad realtorralba de calatrava20930000100000000
248961650.002016.000.000.004.00121.4022.000.004.000.0020120.067.790.400000.810.161.540.000.0000001000100.0060.720.16ciudad realtorralba de calatrava20930000100000000
348961650.002018.000.000.006.00121.4023.000.004.000.0020120.067.790.400000.810.161.540.000.0000001000100.0060.720.16ciudad realtorralba de calatrava20930000100000000
448961650.002020.000.000.008.00121.4024.000.004.000.0020120.067.790.400000.810.161.540.000.0000001000100.0060.720.16ciudad realtorralba de calatrava20930000100000000
548964550.002013.000.000.001.00121.4016.000.004.000.0020120.092.080.400002.140.220.590.000.0000001000100.014.330.16ciudad realtorralba de calatrava20930000100000000
648964550.002015.000.000.003.00121.4022.000.004.000.0020120.092.080.400002.140.220.590.000.0000001000100.014.330.16ciudad realtorralba de calatrava20930000100000000
748964550.002016.000.000.004.00121.4022.000.004.000.0020120.092.080.400002.140.220.590.000.0000001000100.014.330.16ciudad realtorralba de calatrava20930000100000000
848964550.002018.000.000.006.00121.4023.000.004.000.0020120.092.080.400002.140.220.590.000.0000001000100.014.330.16ciudad realtorralba de calatrava20930000100000000
948964550.002020.000.000.008.00121.4024.000.004.000.0020120.092.080.400002.140.220.590.000.0000001000100.014.330.16ciudad realtorralba de calatrava20930000100000000
PipeIdNo_InspectionsNo_IncidentsInspectionYearProbability_ratepreventive_maintenance_rateAge_pipe_at_inspectionpipe_inspected_frequentlyAverage_MonthsLastRevMonthsLastRevrelative_riskaverage_severity_pipeIncidenceYearBuiltDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridleaspectRelative_Thicknesspipe_areaarea_connectionincidence_areaconnection_boolSeverity_highSeverity_mediumSeverity_lowgas_naturalMaterial_Acrylonitrile-Butadiene-StyreneMaterial_CopperMaterial_Fiberglass-Reinforced PlasticMaterial_PolyethyleneMaterial_PolypropyleneDiameter2Length2Pressure2ProvinceTownTownCountAutonomía_AndalucíaAutonomía_AragónAutonomía_Balears (Illes)Autonomía_Castilla y LeónAutonomía_Castilla-La ManchaAutonomía_CataluñaAutonomía_Comunidad ValencianaAutonomía_ExtremaduraAutonomía_GaliciaAutonomía_Madrid (Comunidad de)Autonomía_Navarra (Comunidad Foral de)Autonomía_Rioja (La)Madrid
552774441570470810.002012.000.000.001.00118.0018.000.004.000.0020110.150.7549.50000433.070.000.360.000.0000001100000.020.562450.25alicantefinestrat5780000001000000
552774541653594410.002013.000.000.002.0010.000.000.004.000.0020110.101.0045.00000442.910.000.320.000.0000001100000.011.002025.00la riojabanos de rio tobia8520000000000010
552774641653598810.002013.000.000.002.0010.000.000.004.000.0020110.100.5445.00000818.690.000.170.000.0000001100000.010.292025.00la riojabanos de rio tobia8520000000000010
552774741653603210.002013.000.000.002.0010.000.000.004.000.0020110.100.7545.00000590.550.000.240.000.0000001100000.010.562025.00la riojabanos de rio tobia8520000000000010
552774841653605710.002013.000.000.002.0010.000.000.004.000.0020110.100.5245.00000858.360.000.160.000.0000001100000.010.272025.00la riojabanos de rio tobia8520000000000010
552774941653608710.002013.000.000.002.0010.000.000.004.000.0020110.050.4545.000001951.160.000.070.000.0000001100000.000.212025.00la riojabanos de rio tobia8520000000000010
552775041653641610.002013.000.000.002.0010.000.000.004.000.0020110.100.4845.00000926.600.000.150.000.0000001100000.010.232025.00la riojabanos de rio tobia8520000000000010
552775141653643810.002013.000.000.002.0010.000.000.004.000.0020110.100.4845.00000913.220.000.150.000.0000001100000.010.242025.00la riojabanos de rio tobia8520000000000010
552775241653646010.002013.000.000.002.0010.000.000.004.000.0020110.050.2145.000004218.220.000.030.000.0000001100000.000.042025.00la riojabanos de rio tobia8520000000000010
552775341653648210.002013.000.000.002.0010.000.000.004.000.0020110.050.8045.000001114.250.000.130.000.0000001100000.000.632025.00la riojabanos de rio tobia8520000000000010